Large Language Models (LLMs) are effectively used as task assistants, retrieving essential information to satisfy users' requests. However, a common problem experienced with LLMs is their tendency to provide erroneous or 'hallucinated' responses. Hallucination in LLMs refers to the generation of information that is not based on actual data or knowledge received during the model's…
The field of large language models (LLMs), such as GPT, Claude, and Gemini, has seen rapid advancement, enabling the creation of autonomous agents capable of natural language interactions and executing diverse tasks. These AI agents are increasingly benefiting from the integration of external tools and knowledge sources, which expand their capacity to access and use…
Advances in technology over the past century, specifically the proliferation of computers, has facilitated the development of molecular representations that can be understood by these machines, assisting the process of drug discovery. Initial representations of molecules were simplified, showing only bonds and atoms. However, as the complexity of computational processing increased, more sophisticated representations were…
Software engineering is a rapidly evolving field aimed at systematic design, development, testing, and maintenance of software systems. In recent times, large language models (LLMs) such as GPT-3 have been employed to automate and optimize various software engineering tasks. However, the use of autonomous LLM-based agents has its challenges given their cost and complexity, and…
The increase in the hidden layer width of feedforward (FFW) layers results in linear growth in computational costs and activation memory in transformer architectures. This causes a significant issue in scaling, especially with increasingly complex models. These challenges affect the deployment of large-scale models in real-world applications, including language modeling and natural language processing.
Previously, Mixture…
Language modelling, an essential tool in developing effective natural language processing (NLP) and artificial intelligence (AI) applications, has significantly benefited from advancements in algorithms that understand, generate, and manipulate human language. These advancements have catalyzed large models that can undertake tasks such as translation, summarization, and question answering. However, they face notable challenges, including difficulties…
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of cybersecurity by enhancing both defensive and offensive capabilities. On the defensive end, they are assisting systems to better detect and tackle cyber threats. AI and ML algorithms are proficient in dealing with vast datasets, thereby effectively identifying patterns and anomalies. These techniques have…
A group of researchers from Stanford University, UC San Diego, UC Berkeley, and Meta AI has proposed a new class of sequence modeling layers that blend the expressive hidden state of self-attention mechanisms with the linear complexity of Recurrent Neural Networks (RNNs). These layers are called Test-Time Training (TTT) layers.
Self-attention mechanisms excel at processing extended…
In recent years, the advancement of technology has allowed for the development of computer-verifiable formal languages, further advancing the field of mathematical reasoning. One of these languages, known as Lean, is an instrument employed to validate mathematical theorems, thereby ensuring accuracy and consistency in mathematical outcomes. Scholars are increasingly using Large Language Models (LLMs), specifically…
Large Language Models (LLMs) are advanced Artificial Intelligence tools designed to understand, interpret, and respond to human language in a similar way to human speech. They are currently used in various areas such as customer service, mental health, and healthcare, due to their ability to interact directly with humans. However, recently, researchers from the National…